


'''simple docstring'''


import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import (
				    MODEL_MAPPING,
				    BeitForImageClassification,
				    BeitForMaskedImageModeling,
				    BeitForSemanticSegmentation,
				    BeitModel,
				)
				from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				import PIL
				from PIL import Image

				from transformers import BeitImageProcessor





class        lowercase__    :





							'''simple docstring'''


							def __init__(       self       ,   __snake_case       ,   __snake_case=100       ,   __snake_case=13       ,   __snake_case=30       ,   __snake_case=2       ,   __snake_case=3       ,   __snake_case=True       ,   __snake_case=True       ,   __snake_case=32       ,   __snake_case=4       ,   __snake_case=4       ,   __snake_case=37       ,   __snake_case="gelu"       ,   __snake_case=0.1       ,   __snake_case=0.1       ,   __snake_case=10       ,   __snake_case=0.02       ,   __snake_case=3       ,   __snake_case=None       ,   __snake_case=[0, 1, 2, 3]       ,   ):
											_SCREAMING_SNAKE_CASE					:			Dict							= parent
											_SCREAMING_SNAKE_CASE					:			int							= 100
											_SCREAMING_SNAKE_CASE					:			str							= batch_size
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= image_size
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= patch_size
											_SCREAMING_SNAKE_CASE					:			List[Any]							= num_channels
											_SCREAMING_SNAKE_CASE					:			Dict							= is_training
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= use_labels
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= hidden_size
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= num_hidden_layers
											_SCREAMING_SNAKE_CASE					:			List[Any]							= num_attention_heads
											_SCREAMING_SNAKE_CASE					:			List[str]							= intermediate_size
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= hidden_act
											_SCREAMING_SNAKE_CASE					:			Tuple							= hidden_dropout_prob
											_SCREAMING_SNAKE_CASE					:			str							= attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE					:			List[Any]							= type_sequence_label_size
											_SCREAMING_SNAKE_CASE					:			str							= initializer_range
											_SCREAMING_SNAKE_CASE					:			str							= scope
											_SCREAMING_SNAKE_CASE					:			Any							= out_indices
											_SCREAMING_SNAKE_CASE					:			Tuple							= num_labels

											# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= (image_size // patch_size) ** 2
											_SCREAMING_SNAKE_CASE					:			Tuple							= num_patches + 1






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Any							= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

											_SCREAMING_SNAKE_CASE					:			Optional[int]							= None
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= None
											if self.use_labels:
															_SCREAMING_SNAKE_CASE					:			str							= ids_tensor([self.batch_size]       ,   self.type_sequence_label_size    )
															_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= ids_tensor([self.batch_size, self.image_size, self.image_size]       ,   self.num_labels    )

											_SCREAMING_SNAKE_CASE					:			Dict							= self.get_config()

											return config, pixel_values, labels, pixel_labels






							def UpperCAmelCase_      (       self    ):
											return BeitConfig(
											    vocab_size=self.vocab_size       ,   image_size=self.image_size       ,   patch_size=self.patch_size       ,   num_channels=self.num_channels       ,   hidden_size=self.hidden_size       ,   num_hidden_layers=self.num_hidden_layers       ,   num_attention_heads=self.num_attention_heads       ,   intermediate_size=self.intermediate_size       ,   hidden_act=self.hidden_act       ,   hidden_dropout_prob=self.hidden_dropout_prob       ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,   is_decoder=__snake_case       ,   initializer_range=self.initializer_range       ,   out_indices=self.out_indices       ,   )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case       ,   __snake_case       ,   __snake_case    ):
											_SCREAMING_SNAKE_CASE					:			Dict							= BeitModel(config=__snake_case    )
											model.to(__snake_case    )
											model.eval()
											_SCREAMING_SNAKE_CASE					:			int							= model(__snake_case    )
											self.parent.assertEqual(result.last_hidden_state.shape       ,   (self.batch_size, self.seq_length, self.hidden_size)    )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case       ,   __snake_case       ,   __snake_case    ):
											_SCREAMING_SNAKE_CASE					:			Tuple							= BeitForMaskedImageModeling(config=__snake_case    )
											model.to(__snake_case    )
											model.eval()
											_SCREAMING_SNAKE_CASE					:			int							= model(__snake_case    )
											self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.seq_length - 1, self.vocab_size)    )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case       ,   __snake_case       ,   __snake_case    ):
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= self.type_sequence_label_size
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= BeitForImageClassification(__snake_case    )
											model.to(__snake_case    )
											model.eval()
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= model(__snake_case       ,   labels=__snake_case    )
											self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.type_sequence_label_size)    )

											# test greyscale images
											_SCREAMING_SNAKE_CASE					:			List[Any]							= 1
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= BeitForImageClassification(__snake_case    )
											model.to(__snake_case    )
											model.eval()

											_SCREAMING_SNAKE_CASE					:			List[Any]							= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
											_SCREAMING_SNAKE_CASE					:			List[Any]							= model(__snake_case       ,   labels=__snake_case    )
											self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.type_sequence_label_size)    )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case       ,   __snake_case       ,   __snake_case    ):
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= self.num_labels
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= BeitForSemanticSegmentation(__snake_case    )
											model.to(__snake_case    )
											model.eval()
											_SCREAMING_SNAKE_CASE					:			str							= model(__snake_case    )
											self.parent.assertEqual(
											    result.logits.shape       ,   (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)    )
											_SCREAMING_SNAKE_CASE					:			str							= model(__snake_case       ,   labels=__snake_case    )
											self.parent.assertEqual(
											    result.logits.shape       ,   (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Tuple							= self.prepare_config_and_inputs()
											_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= config_and_inputs
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= {"""pixel_values""": pixel_values}
											return config, inputs_dict





@require_torch
class        lowercase__    (		_snake_case	,	_snake_case	,	unittest.TestCase   ):





							'''simple docstring'''


							A_				: Optional[int]       =    (
							    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
							    if is_torch_available()
							    else ()
							)
							A_				: int       =    (
							    {
							        """feature-extraction""": BeitModel,
							        """image-classification""": BeitForImageClassification,
							        """image-segmentation""": BeitForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							A_				: int       =    False
							A_				: List[str]       =    False
							A_				: Tuple       =    False






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			int							= BeitModelTester(self    )
											_SCREAMING_SNAKE_CASE					:			Dict							= ConfigTester(self       ,   config_class=__snake_case       ,   has_text_modality=__snake_case       ,   hidden_size=37    )






							def UpperCAmelCase_      (       self    ):
											self.config_tester.run_common_tests()






							@unittest.skip(reason="""BEiT does not use inputs_embeds"""    )
							def UpperCAmelCase_      (       self    ):
											pass






							@require_torch_multi_gpu
							@unittest.skip(reason="""BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""    )
							def UpperCAmelCase_      (       self    ):
											pass






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[Any]							= self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE					:			Any							= model_class(__snake_case    )
															self.assertIsInstance(model.get_input_embeddings()       ,   (nn.Module)    )
															_SCREAMING_SNAKE_CASE					:			str							= model.get_output_embeddings()
															self.assertTrue(x is None or isinstance(__snake_case       ,   nn.Linear    )    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			str							= self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE					:			List[Any]							= model_class(__snake_case    )
															_SCREAMING_SNAKE_CASE					:			Optional[Any]							= inspect.signature(model.forward    )
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															_SCREAMING_SNAKE_CASE					:			Optional[int]							= [*signature.parameters.keys()]

															_SCREAMING_SNAKE_CASE					:			Dict							= ["""pixel_values"""]
															self.assertListEqual(arg_names[:1]       ,   __snake_case    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Tuple							= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__snake_case    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Tuple							= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*__snake_case    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			str							= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__snake_case    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			int							= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_semantic_segmentation(*__snake_case    )






							def UpperCAmelCase_      (       self    ):
											if not self.model_tester.is_training:
															return

											_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Dict							= self.model_tester.prepare_config_and_inputs_for_common()
											_SCREAMING_SNAKE_CASE					:			Any							= True

											for model_class in self.all_model_classes:
															# we don't test BeitForMaskedImageModeling
															if model_class in [*get_values(__snake_case    ), BeitForMaskedImageModeling]:
																			continue

															_SCREAMING_SNAKE_CASE					:			Optional[Any]							= model_class(__snake_case    )
															model.to(__snake_case    )
															model.train()
															_SCREAMING_SNAKE_CASE					:			int							= self._prepare_for_class(__snake_case       ,   __snake_case       ,   return_labels=__snake_case    )
															_SCREAMING_SNAKE_CASE					:			Optional[int]							= model(**__snake_case    ).loss
															loss.backward()






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Tuple							= self.model_tester.prepare_config_and_inputs_for_common()
											if not self.model_tester.is_training:
															return

											_SCREAMING_SNAKE_CASE					:			Dict							= False
											_SCREAMING_SNAKE_CASE					:			str							= True

											for model_class in self.all_model_classes:
															# we don't test BeitForMaskedImageModeling
															if (
															    model_class in [*get_values(__snake_case    ), BeitForMaskedImageModeling]
															    or not model_class.supports_gradient_checkpointing
															):
																			continue

															_SCREAMING_SNAKE_CASE					:			Optional[Any]							= model_class(__snake_case    )
															model.gradient_checkpointing_enable()
															model.to(__snake_case    )
															model.train()
															_SCREAMING_SNAKE_CASE					:			str							= self._prepare_for_class(__snake_case       ,   __snake_case       ,   return_labels=__snake_case    )
															_SCREAMING_SNAKE_CASE					:			Optional[int]							= model(**__snake_case    ).loss
															loss.backward()






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			str							= self.model_tester.prepare_config_and_inputs_for_common()

											_SCREAMING_SNAKE_CASE					:			List[Any]							= _config_zero_init(__snake_case    )
											for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= model_class(config=__snake_case    )
															for name, param in model.named_parameters():
																			# we skip lambda parameters as these require special initial values
																			# determined by config.layer_scale_init_value
																			if "lambda" in name:
																							continue
																			if param.requires_grad:
																							self.assertIn(
																							    ((param.data.mean() * 1e9).round() / 1e9).item()       ,   [0.0, 1.0]       ,   msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""       ,   )






							@slow
							def UpperCAmelCase_      (       self    ):
											for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_SCREAMING_SNAKE_CASE					:			List[str]							= BeitModel.from_pretrained(__snake_case    )
															self.assertIsNotNone(__snake_case    )







def      snake_case_  (    ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			List[Any]							= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
				return image







@require_torch
@require_vision
class        lowercase__    (		unittest.TestCase   ):





							'''simple docstring'''


							@cached_property
							def UpperCAmelCase_      (       self    ):
											return BeitImageProcessor.from_pretrained("""microsoft/beit-base-patch16-224"""    ) if is_vision_available() else None






							@slow
							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= BeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k"""    ).to(__snake_case    )

											_SCREAMING_SNAKE_CASE					:			List[Any]							= self.default_image_processor
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= prepare_img()
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= image_processor(images=__snake_case       ,   return_tensors="""pt"""    ).pixel_values.to(__snake_case    )

											# prepare bool_masked_pos
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= torch.ones((1, 196)       ,   dtype=torch.bool    ).to(__snake_case    )

											# forward pass
											with torch.no_grad():
															_SCREAMING_SNAKE_CASE					:			List[Any]							= model(pixel_values=__snake_case       ,   bool_masked_pos=__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Dict							= outputs.logits

											# verify the logits
											_SCREAMING_SNAKE_CASE					:			Tuple							= torch.Size((1, 196, 8192)    )
											self.assertEqual(logits.shape       ,   __snake_case    )

											_SCREAMING_SNAKE_CASE					:			List[str]							= torch.tensor(
											    [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]    ).to(__snake_case    )

											self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]       ,   __snake_case       ,   atol=1e-2    )    )






							@slow
							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			int							= BeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224"""    ).to(__snake_case    )

											_SCREAMING_SNAKE_CASE					:			List[str]							= self.default_image_processor
											_SCREAMING_SNAKE_CASE					:			List[Any]							= prepare_img()
											_SCREAMING_SNAKE_CASE					:			List[str]							= image_processor(images=__snake_case       ,   return_tensors="""pt"""    ).to(__snake_case    )

											# forward pass
											with torch.no_grad():
															_SCREAMING_SNAKE_CASE					:			List[Any]							= model(**__snake_case    )
											_SCREAMING_SNAKE_CASE					:			int							= outputs.logits

											# verify the logits
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= torch.Size((1, 1000)    )
											self.assertEqual(logits.shape       ,   __snake_case    )

											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= torch.tensor([-1.2385, -1.0987, -1.0108]    ).to(__snake_case    )

											self.assertTrue(torch.allclose(logits[0, :3]       ,   __snake_case       ,   atol=1e-4    )    )

											_SCREAMING_SNAKE_CASE					:			Optional[int]							= 281
											self.assertEqual(logits.argmax(-1    ).item()       ,   __snake_case    )






							@slow
							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Dict							= BeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k"""    ).to(
											    __snake_case    )

											_SCREAMING_SNAKE_CASE					:			Any							= self.default_image_processor
											_SCREAMING_SNAKE_CASE					:			str							= prepare_img()
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= image_processor(images=__snake_case       ,   return_tensors="""pt"""    ).to(__snake_case    )

											# forward pass
											with torch.no_grad():
															_SCREAMING_SNAKE_CASE					:			int							= model(**__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Tuple							= outputs.logits

											# verify the logits
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= torch.Size((1, 2_1841)    )
											self.assertEqual(logits.shape       ,   __snake_case    )

											_SCREAMING_SNAKE_CASE					:			Optional[int]							= torch.tensor([1.6881, -0.2787, 0.5901]    ).to(__snake_case    )

											self.assertTrue(torch.allclose(logits[0, :3]       ,   __snake_case       ,   atol=1e-4    )    )

											_SCREAMING_SNAKE_CASE					:			Any							= 2396
											self.assertEqual(logits.argmax(-1    ).item()       ,   __snake_case    )






							@slow
							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640"""    )
											_SCREAMING_SNAKE_CASE					:			str							= model.to(__snake_case    )

											_SCREAMING_SNAKE_CASE					:			List[str]							= BeitImageProcessor(do_resize=__snake_case       ,   size=640       ,   do_center_crop=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			str							= load_dataset("""hf-internal-testing/fixtures_ade20k"""       ,   split="""test"""    )
											_SCREAMING_SNAKE_CASE					:			str							= Image.open(ds[0]["""file"""]    )
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= image_processor(images=__snake_case       ,   return_tensors="""pt"""    ).to(__snake_case    )

											# forward pass
											with torch.no_grad():
															_SCREAMING_SNAKE_CASE					:			List[str]							= model(**__snake_case    )
											_SCREAMING_SNAKE_CASE					:			List[str]							= outputs.logits

											# verify the logits
											_SCREAMING_SNAKE_CASE					:			List[str]							= torch.Size((1, 150, 160, 160)    )
											self.assertEqual(logits.shape       ,   __snake_case    )

											_SCREAMING_SNAKE_CASE					:			Optional[int]							= version.parse(PIL.__version__    ) < version.parse("""9.0.0"""    )

											if is_pillow_less_than_a:
															_SCREAMING_SNAKE_CASE					:			Dict							= torch.tensor(
															    [
															        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
															        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
															        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
															    ]       ,   device=__snake_case       ,   )
											else:
															_SCREAMING_SNAKE_CASE					:			Optional[Any]							= torch.tensor(
															    [
															        [[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
															        [[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
															        [[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
															    ]       ,   device=__snake_case       ,   )

											self.assertTrue(torch.allclose(logits[0, :3, :3, :3]       ,   __snake_case       ,   atol=1e-4    )    )






							@slow
							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Tuple							= BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640"""    )
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= model.to(__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Tuple							= BeitImageProcessor(do_resize=__snake_case       ,   size=640       ,   do_center_crop=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Optional[int]							= load_dataset("""hf-internal-testing/fixtures_ade20k"""       ,   split="""test"""    )
											_SCREAMING_SNAKE_CASE					:			Any							= Image.open(ds[0]["""file"""]    )
											_SCREAMING_SNAKE_CASE					:			int							= image_processor(images=__snake_case       ,   return_tensors="""pt"""    ).to(__snake_case    )

											# forward pass
											with torch.no_grad():
															_SCREAMING_SNAKE_CASE					:			Optional[int]							= model(**__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Optional[int]							= outputs.logits.detach().cpu()

											_SCREAMING_SNAKE_CASE					:			Tuple							= image_processor.post_process_semantic_segmentation(outputs=__snake_case       ,   target_sizes=[(500, 300)]    )
											_SCREAMING_SNAKE_CASE					:			int							= torch.Size((500, 300)    )
											self.assertEqual(segmentation[0].shape       ,   __snake_case    )

											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= image_processor.post_process_semantic_segmentation(outputs=__snake_case    )
											_SCREAMING_SNAKE_CASE					:			int							= torch.Size((160, 160)    )
											self.assertEqual(segmentation[0].shape       ,   __snake_case    )








'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_						:  str				  =						logging.get_logger(__name__)

UpperCAmelCase_						:  Optional[int]				  =						{
    'SCUT-DLVCLab/lilt-roberta-en-base': (
        'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'
    ),
}





class        lowercase__    (		_snake_case   ):





							'''simple docstring'''


							A_				: List[str]       =    """lilt"""






							def __init__(       self       ,   __snake_case=3_0522       ,   __snake_case=768       ,   __snake_case=12       ,   __snake_case=12       ,   __snake_case=3072       ,   __snake_case="gelu"       ,   __snake_case=0.1       ,   __snake_case=0.1       ,   __snake_case=512       ,   __snake_case=2       ,   __snake_case=0.02       ,   __snake_case=1e-12       ,   __snake_case=0       ,   __snake_case="absolute"       ,   __snake_case=None       ,   __snake_case=4       ,   __snake_case=1024       ,   **__snake_case       ,   ):
											super().__init__(pad_token_id=__snake_case       ,   **__snake_case    )

											_SCREAMING_SNAKE_CASE					:			str							= vocab_size
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= hidden_size
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= num_hidden_layers
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= num_attention_heads
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= hidden_act
											_SCREAMING_SNAKE_CASE					:			Dict							= intermediate_size
											_SCREAMING_SNAKE_CASE					:			Tuple							= hidden_dropout_prob
											_SCREAMING_SNAKE_CASE					:			List[str]							= attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= max_position_embeddings
											_SCREAMING_SNAKE_CASE					:			Dict							= type_vocab_size
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= initializer_range
											_SCREAMING_SNAKE_CASE					:			Tuple							= layer_norm_eps
											_SCREAMING_SNAKE_CASE					:			str							= position_embedding_type
											_SCREAMING_SNAKE_CASE					:			List[str]							= classifier_dropout
											_SCREAMING_SNAKE_CASE					:			int							= channel_shrink_ratio
											_SCREAMING_SNAKE_CASE					:			str							= max_ad_position_embeddings




